Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations1125
Missing cells149
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory114.4 KiB
Average record size in memory104.1 B

Variable types

Categorical4
Numeric9

Alerts

fea_1 is highly overall correlated with fea_6High correlation
fea_6 is highly overall correlated with fea_1 and 1 other fieldsHigh correlation
id is highly overall correlated with fea_6High correlation
fea_5 is highly imbalanced (63.0%) Imbalance
fea_2 has 149 (13.2%) missing values Missing
id has unique values Unique

Reproduction

Analysis started2025-03-12 01:56:30.318391
Analysis finished2025-03-12 01:56:35.363058
Duration5.04 seconds
Software versionydata-profiling vv4.13.0
Download configurationconfig.json

Variables

label
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
0
900 
1
225 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1125
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 900
80.0%
1 225
 
20.0%

Length

2025-03-11T22:56:35.402703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T22:56:35.465266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 900
80.0%
1 225
 
20.0%

Most occurring characters

ValueCountFrequency (%)
0 900
80.0%
1 225
 
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 900
80.0%
1 225
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 900
80.0%
1 225
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 900
80.0%
1 225
 
20.0%

id
Real number (ℝ)

High correlation  Unique 

Distinct1125
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57836771
Minimum54982353
Maximum59006239
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-03-11T22:56:35.528994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum54982353
5-th percentile54984218
Q154990497
median58989748
Q358997994
95-th percentile59004536
Maximum59006239
Range4023886
Interquartile range (IQR)4007497

Descriptive statistics

Standard deviation1817150.4
Coefficient of variation (CV)0.0314186
Kurtosis-1.1319137
Mean57836771
Median Absolute Deviation (MAD)9940
Skewness-0.93276429
Sum6.5066368 × 1010
Variance3.3020355 × 1012
MonotonicityNot monotonic
2025-03-11T22:56:35.677649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54982665 1
 
0.1%
58991343 1
 
0.1%
54988970 1
 
0.1%
54991614 1
 
0.1%
58989779 1
 
0.1%
59003701 1
 
0.1%
59005448 1
 
0.1%
58999966 1
 
0.1%
58986443 1
 
0.1%
58984421 1
 
0.1%
Other values (1115) 1115
99.1%
ValueCountFrequency (%)
54982353 1
0.1%
54982356 1
0.1%
54982387 1
0.1%
54982463 1
0.1%
54982530 1
0.1%
54982549 1
0.1%
54982579 1
0.1%
54982665 1
0.1%
54982697 1
0.1%
54982721 1
0.1%
ValueCountFrequency (%)
59006239 1
0.1%
59006219 1
0.1%
59006193 1
0.1%
59006139 1
0.1%
59005995 1
0.1%
59005917 1
0.1%
59005881 1
0.1%
59005880 1
0.1%
59005871 1
0.1%
59005860 1
0.1%

fea_1
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4826667
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-03-11T22:56:35.730394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q14
median5
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.3833375
Coefficient of variation (CV)0.25231108
Kurtosis-1.1810284
Mean5.4826667
Median Absolute Deviation (MAD)1
Skewness-0.10437923
Sum6168
Variance1.9136228
MonotonicityNot monotonic
2025-03-11T22:56:35.772794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
7 476
42.3%
4 377
33.5%
5 261
23.2%
1 7
 
0.6%
6 2
 
0.2%
2 2
 
0.2%
ValueCountFrequency (%)
1 7
 
0.6%
2 2
 
0.2%
4 377
33.5%
5 261
23.2%
6 2
 
0.2%
7 476
42.3%
ValueCountFrequency (%)
7 476
42.3%
6 2
 
0.2%
5 261
23.2%
4 377
33.5%
2 2
 
0.2%
1 7
 
0.6%

fea_2
Real number (ℝ)

Missing 

Distinct158
Distinct (%)16.2%
Missing149
Missing (%)13.2%
Infinite0
Infinite (%)0.0%
Mean1283.9114
Minimum1116.5
Maximum1481
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-03-11T22:56:35.831069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1116.5
5-th percentile1214
Q11244
median1281.5
Q31314.5
95-th percentile1371.5
Maximum1481
Range364.5
Interquartile range (IQR)70.5

Descriptive statistics

Standard deviation51.764022
Coefficient of variation (CV)0.040317441
Kurtosis0.58544031
Mean1283.9114
Median Absolute Deviation (MAD)36
Skewness0.41207622
Sum1253097.5
Variance2679.5139
MonotonicityNot monotonic
2025-03-11T22:56:35.905136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1241 37
 
3.3%
1214 27
 
2.4%
1305.5 23
 
2.0%
1287.5 21
 
1.9%
1223 21
 
1.9%
1304 20
 
1.8%
1257.5 19
 
1.7%
1266.5 19
 
1.7%
1272.5 18
 
1.6%
1239.5 17
 
1.5%
Other values (148) 754
67.0%
(Missing) 149
 
13.2%
ValueCountFrequency (%)
1116.5 1
0.1%
1125.5 1
0.1%
1130 1
0.1%
1137.5 1
0.1%
1148 1
0.1%
1163 1
0.1%
1164.5 1
0.1%
1166 1
0.1%
1170.5 2
0.2%
1179.5 2
0.2%
ValueCountFrequency (%)
1481 1
 
0.1%
1475 1
 
0.1%
1469 2
0.2%
1455.5 1
 
0.1%
1449.5 1
 
0.1%
1443.5 2
0.2%
1425.5 1
 
0.1%
1419.5 2
0.2%
1415 3
0.3%
1413.5 1
 
0.1%

fea_3
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
3
684 
1
309 
2
132 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1125
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
3 684
60.8%
1 309
27.5%
2 132
 
11.7%

Length

2025-03-11T22:56:35.967580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T22:56:36.002452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 684
60.8%
1 309
27.5%
2 132
 
11.7%

Most occurring characters

ValueCountFrequency (%)
3 684
60.8%
1 309
27.5%
2 132
 
11.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 684
60.8%
1 309
27.5%
2 132
 
11.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 684
60.8%
1 309
27.5%
2 132
 
11.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 684
60.8%
1 309
27.5%
2 132
 
11.7%

fea_4
Real number (ℝ)

Distinct229
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120883.56
Minimum15000
Maximum1200000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-03-11T22:56:36.057520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15000
5-th percentile39000
Q172000
median102000
Q3139000
95-th percentile282000
Maximum1200000
Range1185000
Interquartile range (IQR)67000

Descriptive statistics

Standard deviation88445.229
Coefficient of variation (CV)0.73165641
Kurtosis32.210409
Mean120883.56
Median Absolute Deviation (MAD)32000
Skewness4.174755
Sum1.35994 × 108
Variance7.8225585 × 109
MonotonicityNot monotonic
2025-03-11T22:56:36.131274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35000 34
 
3.0%
50000 25
 
2.2%
90000 19
 
1.7%
150000 19
 
1.7%
110000 18
 
1.6%
100000 18
 
1.6%
130000 17
 
1.5%
68000 16
 
1.4%
56000 15
 
1.3%
71000 15
 
1.3%
Other values (219) 929
82.6%
ValueCountFrequency (%)
15000 2
 
0.2%
30000 14
1.2%
34000 1
 
0.1%
35000 34
3.0%
38000 3
 
0.3%
39000 4
 
0.4%
41000 1
 
0.1%
42000 2
 
0.2%
43000 1
 
0.1%
44000 2
 
0.2%
ValueCountFrequency (%)
1200000 1
 
0.1%
1000000 1
 
0.1%
550000 1
 
0.1%
546000 1
 
0.1%
500000 8
0.7%
489000 1
 
0.1%
488000 1
 
0.1%
483000 1
 
0.1%
468000 1
 
0.1%
458000 1
 
0.1%

fea_5
Categorical

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
2
1045 
1
 
80

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1125
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 1045
92.9%
1 80
 
7.1%

Length

2025-03-11T22:56:36.193579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T22:56:36.224953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 1045
92.9%
1 80
 
7.1%

Most occurring characters

ValueCountFrequency (%)
2 1045
92.9%
1 80
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1045
92.9%
1 80
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1045
92.9%
1 80
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1045
92.9%
1 80
 
7.1%

fea_6
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.872
Minimum3
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-03-11T22:56:36.256797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile8
Q18
median11
Q311
95-th percentile15
Maximum16
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.6764373
Coefficient of variation (CV)0.24617709
Kurtosis-0.85633027
Mean10.872
Median Absolute Deviation (MAD)3
Skewness0.30198963
Sum12231
Variance7.1633167
MonotonicityNot monotonic
2025-03-11T22:56:36.304448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
11 465
41.3%
8 375
33.3%
15 259
23.0%
12 11
 
1.0%
4 4
 
0.4%
5 3
 
0.3%
6 2
 
0.2%
9 2
 
0.2%
3 2
 
0.2%
16 2
 
0.2%
ValueCountFrequency (%)
3 2
 
0.2%
4 4
 
0.4%
5 3
 
0.3%
6 2
 
0.2%
8 375
33.3%
9 2
 
0.2%
11 465
41.3%
12 11
 
1.0%
15 259
23.0%
16 2
 
0.2%
ValueCountFrequency (%)
16 2
 
0.2%
15 259
23.0%
12 11
 
1.0%
11 465
41.3%
9 2
 
0.2%
8 375
33.3%
6 2
 
0.2%
5 3
 
0.3%
4 4
 
0.4%
3 2
 
0.2%

fea_7
Real number (ℝ)

Distinct10
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8328889
Minimum-1
Maximum10
Zeros0
Zeros (%)0.0%
Negative170
Negative (%)15.1%
Memory size8.9 KiB
2025-03-11T22:56:36.348496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q15
median5
Q35
95-th percentile9
Maximum10
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.9711817
Coefficient of variation (CV)0.61478379
Kurtosis0.053229923
Mean4.8328889
Median Absolute Deviation (MAD)0
Skewness-0.60681621
Sum5437
Variance8.8279209
MonotonicityNot monotonic
2025-03-11T22:56:36.396991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 689
61.2%
9 212
 
18.8%
-1 170
 
15.1%
2 17
 
1.5%
8 9
 
0.8%
3 9
 
0.8%
4 7
 
0.6%
7 6
 
0.5%
10 5
 
0.4%
1 1
 
0.1%
ValueCountFrequency (%)
-1 170
 
15.1%
1 1
 
0.1%
2 17
 
1.5%
3 9
 
0.8%
4 7
 
0.6%
5 689
61.2%
7 6
 
0.5%
8 9
 
0.8%
9 212
 
18.8%
10 5
 
0.4%
ValueCountFrequency (%)
10 5
 
0.4%
9 212
 
18.8%
8 9
 
0.8%
7 6
 
0.5%
5 689
61.2%
4 7
 
0.6%
3 9
 
0.8%
2 17
 
1.5%
1 1
 
0.1%
-1 170
 
15.1%

fea_8
Real number (ℝ)

Distinct52
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.80267
Minimum64
Maximum115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-03-11T22:56:36.460102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum64
5-th percentile80
Q190
median105
Q3111
95-th percentile114
Maximum115
Range51
Interquartile range (IQR)21

Descriptive statistics

Standard deviation11.988955
Coefficient of variation (CV)0.1189349
Kurtosis-0.28496104
Mean100.80267
Median Absolute Deviation (MAD)7
Skewness-0.83900783
Sum113403
Variance143.73505
MonotonicityNot monotonic
2025-03-11T22:56:36.531203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110 98
 
8.7%
112 93
 
8.3%
100 80
 
7.1%
113 70
 
6.2%
114 65
 
5.8%
105 58
 
5.2%
111 52
 
4.6%
109 42
 
3.7%
90 42
 
3.7%
107 39
 
3.5%
Other values (42) 486
43.2%
ValueCountFrequency (%)
64 7
0.6%
65 1
 
0.1%
66 1
 
0.1%
67 2
 
0.2%
68 2
 
0.2%
69 1
 
0.1%
70 2
 
0.2%
71 2
 
0.2%
72 2
 
0.2%
73 3
0.3%
ValueCountFrequency (%)
115 15
 
1.3%
114 65
5.8%
113 70
6.2%
112 93
8.3%
111 52
4.6%
110 98
8.7%
109 42
3.7%
108 28
 
2.5%
107 39
 
3.5%
106 18
 
1.6%

fea_9
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
5
521 
4
318 
3
278 
1
 
7
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1125
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row5
2nd row3
3rd row5
4th row3
5th row4

Common Values

ValueCountFrequency (%)
5 521
46.3%
4 318
28.3%
3 278
24.7%
1 7
 
0.6%
2 1
 
0.1%

Length

2025-03-11T22:56:36.591240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T22:56:36.630733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
5 521
46.3%
4 318
28.3%
3 278
24.7%
1 7
 
0.6%
2 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
5 521
46.3%
4 318
28.3%
3 278
24.7%
1 7
 
0.6%
2 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 521
46.3%
4 318
28.3%
3 278
24.7%
1 7
 
0.6%
2 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 521
46.3%
4 318
28.3%
3 278
24.7%
1 7
 
0.6%
2 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 521
46.3%
4 318
28.3%
3 278
24.7%
1 7
 
0.6%
2 1
 
0.1%

fea_10
Real number (ℝ)

Distinct280
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164618.5
Minimum60000
Maximum650070
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-03-11T22:56:36.687046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum60000
5-th percentile60005
Q160044
median72000
Q3151307
95-th percentile450081
Maximum650070
Range590070
Interquartile range (IQR)91263

Descriptive statistics

Standard deviation152520.49
Coefficient of variation (CV)0.92650882
Kurtosis0.14193148
Mean164618.5
Median Absolute Deviation (MAD)11984
Skewness1.2698434
Sum1.8519581 × 108
Variance2.3262499 × 1010
MonotonicityNot monotonic
2025-03-11T22:56:36.757320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
151300 128
 
11.4%
72000 103
 
9.2%
72001 40
 
3.6%
60000 26
 
2.3%
60019 23
 
2.0%
60091 23
 
2.0%
71000 21
 
1.9%
60018 17
 
1.5%
60014 17
 
1.5%
60036 17
 
1.5%
Other values (270) 710
63.1%
ValueCountFrequency (%)
60000 26
2.3%
60001 10
 
0.9%
60002 5
 
0.4%
60004 10
 
0.9%
60005 11
1.0%
60006 3
 
0.3%
60007 5
 
0.4%
60008 3
 
0.3%
60010 1
 
0.1%
60011 2
 
0.2%
ValueCountFrequency (%)
650070 1
0.1%
650018 1
0.1%
650005 1
0.1%
591044 1
0.1%
591017 1
0.1%
591003 1
0.1%
591001 1
0.1%
552104 2
0.2%
551201 1
0.1%
550115 1
0.1%

fea_11
Real number (ℝ)

Distinct266
Distinct (%)23.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134.999
Minimum1
Maximum707.10678
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-03-11T22:56:36.826957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median173.20508
Q3202.48457
95-th percentile291.47586
Maximum707.10678
Range706.10678
Interquartile range (IQR)201.48457

Descriptive statistics

Standard deviation112.6168
Coefficient of variation (CV)0.83420465
Kurtosis0.7591074
Mean134.999
Median Absolute Deviation (MAD)50.401717
Skewness0.36524058
Sum151873.88
Variance12682.543
MonotonicityNot monotonic
2025-03-11T22:56:36.897967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 407
36.2%
200 101
 
9.0%
173.2050808 81
 
7.2%
223.6067977 50
 
4.4%
187.0828693 46
 
4.1%
158.113883 33
 
2.9%
212.1320344 30
 
2.7%
316.227766 18
 
1.6%
244.9489743 14
 
1.2%
204.9390153 11
 
1.0%
Other values (256) 334
29.7%
ValueCountFrequency (%)
1 407
36.2%
3.16227766 1
 
0.1%
105.9008971 1
 
0.1%
118.6844556 1
 
0.1%
122.4744871 1
 
0.1%
134.9555482 1
 
0.1%
141.4213562 6
 
0.5%
145.6021978 1
 
0.1%
153.2677396 1
 
0.1%
153.3916556 1
 
0.1%
ValueCountFrequency (%)
707.1067812 1
0.1%
692.820323 1
0.1%
632.455532 1
0.1%
626.8971207 1
0.1%
547.7225575 1
0.1%
538.5387637 1
0.1%
500 1
0.1%
492.6601263 1
0.1%
445.0382006 1
0.1%
444.4738462 1
0.1%

Interactions

2025-03-11T22:56:34.708076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:30.573955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:31.137728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:31.630471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:32.215403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:32.712457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:33.199471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:33.689613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:34.223410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:34.767477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:30.650740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:31.196193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:31.693023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:32.273929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:32.770258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:33.255436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:33.745775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:34.281557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:34.822290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:30.709315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:31.249115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:31.750808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:32.329373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:32.823583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:33.307773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:33.797123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:34.335270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:34.881450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:30.774966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:31.306609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:31.809312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:32.387640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:32.879200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:33.363325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:33.851190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:34.392085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:34.937764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:30.834347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:31.362417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:31.867657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:32.441961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:32.934069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:33.416829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:33.903462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:34.446436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:34.992095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:30.897239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:31.417297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:31.923188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:32.495609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:32.986183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:33.467826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:34.024589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:34.499475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:35.045785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:30.964356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:31.470408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:31.979210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:32.549168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:33.038172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:33.534372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:34.073810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:34.551436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:35.097610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:31.019568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:31.521357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:32.033462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:32.601808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:33.090213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:33.583599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:34.120718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:34.602009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:35.152864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:31.077971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:31.575497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:32.156770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:32.655526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:33.143194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:33.635999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:34.171412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:56:34.653802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-11T22:56:37.033195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
fea_1fea_10fea_11fea_2fea_3fea_4fea_5fea_6fea_7fea_8fea_9idlabel
fea_11.0000.1020.087-0.0070.151-0.0100.0380.550-0.0450.0340.063-0.3520.020
fea_100.1021.0000.207-0.0340.148-0.0400.0960.162-0.1480.1630.135-0.0850.000
fea_110.0870.2071.0000.0720.1600.0840.1580.1350.0330.1050.029-0.0730.000
fea_2-0.007-0.0340.0721.0000.3110.4730.000-0.008-0.000-0.0060.0550.0050.072
fea_30.1510.1480.1600.3111.0000.1410.0000.1460.2170.0430.1330.0000.071
fea_4-0.010-0.0400.0840.4730.1411.0000.000-0.0870.023-0.0760.085-0.0100.086
fea_50.0380.0960.1580.0000.0000.0001.0000.0270.0700.1860.0000.0310.000
fea_60.5500.1620.135-0.0080.146-0.0870.0271.000-0.0400.0410.143-0.5710.000
fea_7-0.045-0.1480.033-0.0000.2170.0230.070-0.0401.0000.0930.0590.0060.000
fea_80.0340.1630.105-0.0060.043-0.0760.1860.0410.0931.0000.2750.0130.000
fea_90.0630.1350.0290.0550.1330.0850.0000.1430.0590.2751.0000.0000.000
id-0.352-0.085-0.0730.0050.000-0.0100.031-0.5710.0060.0130.0001.0000.000
label0.0200.0000.0000.0720.0710.0860.0000.0000.0000.0000.0000.0001.000

Missing values

2025-03-11T22:56:35.239093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-11T22:56:35.314650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

labelidfea_1fea_2fea_3fea_4fea_5fea_6fea_7fea_8fea_9fea_10fea_11
015498266551245.5377000.021551095151300244.948974
105900477941277.01113000.028-11003341759207.173840
205899086271298.01110000.0211-11015720011.000000
315899516871335.51151000.021151103600841.000000
40549873207NaN259000.021151084450081197.403141
505900599561217.0356000.026-11003600911.000000
615900191741304.0335000.0289855600691.000000
715498478951256.0378000.0215-11113600301.000000
805898455751323.53218000.021551124151300282.842713
90549904974NaN235000.0285101360029237.301496
labelidfea_1fea_2fea_3fea_4fea_5fea_6fea_7fea_8fea_9fea_10fea_11
111505899683771235.0356000.0211-11144151300206.888859
111605498926441343.03110000.02821055600431.000000
11170590010314NaN258000.02851005151300196.214169
111805899206371137.5388000.0211-11074450081158.113883
111905498581671320.53108000.021151104510068248.997992
112005898819651289.01173000.021551123350702200.000000
11210589879265NaN250000.021551084450000169.000000
112205899538171220.0376000.02112905710021.000000
112305899805441250.03137000.0285905720001.000000
112405498978141415.0393000.02851134151300273.861279